PARKBENCH MATRIX KERNEL BENCHMARKS
The PARKBENCH suite includes five matrix kernels:

Dense matrix multiply. Communication involves broadcast of data
along rows of mesh, and periodic shift along column direction (or vice
versa).

Transpose. Matrix transpose is an important benchmark because it
exercises the communications of computer heavily on a realistic problem
where pairs of processors communicate with each other simultaneously.
It is a useful test of the total communications capacity of the
network.

Dense LU factorization with partial pivoting.
Searching for a pivot
is basically a reduction operation within one column of the processor
mesh. Exchange of pivot rows is a pointtopoint communication. Update
phase requires data to be broadcast along rows and columns of the
processor mesh.

QR Decomposition. In this benchmark parallelization is achieved by
distribution of rows on a logical grid of processors using block
interleaving.

Matrix tridiagonalization, for eigenvalue computations of symmetric
matrices.
These kernels may be obtained in the current
distribution
from the
netlib repository.
PARKBENCH kernels page
Last Modified May 14, 1996